In this paper we present the development of an artificial neural network that uses surface EMG data from two forearm muscles to classify hand movements and gestures. We trained our network to classify three different sets of movements, using EMG data from six healthy subjects. We were able to achieve hit rates of above 99% in the training sets and hit rates of above 85% in all three test sets, with a maximum of 88.8% for the second movement set. Advantages of the proposed method include small number of electrodes, reduced complexity, computational cost and response time.